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Single-variable methods estimate missing values through available measurements of the same environmental variable (e.g., NO2, CO or O3).
Besides, most methods estimate missing values without considering the imputation sequence among incomplete genes.
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In the field of texture reconstruction, not only the methods based on the texture synthesis but also many methods, which estimate missing intensities by utilizing statistical features of known textures within a target image as training patterns, have been proposed.
Many methods to estimate missing rainfall data have been proposed in literature and, among these, most are based on spatial interpolation algorithms.
However, as data may not be missing at random, we will conduct a secondary analysis using multiple imputation methods to estimate missing data [ 23].
In other words, if a gene had four properties, A, B, C, and D, and B was missing, then the posterior probability of HI was taken from the model that considered the properties A, C, and D. Imputation methods to estimate missing values were also examined.
We applied this method to estimate missing logs of sonic and density.
All analyses conducted in MPlus 6.0, using Full-information Maximum Likelihood method to estimate missing data.
Reliabilities calculated using SPSS v. 23; all other analyses conducted in MPlus 6.0, using Full-information Maximum Likelihood method to estimate missing data T-R teacher report.
Obviously, this type of method could estimate missing values accurately if the data matrix contains rich local structure.
These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia.
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